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      <title>AI 大模型的技术趋势 - 交互式测验</title>
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      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>AI 大模型的技术趋势 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
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  {
    "question": "根据文档，推动AI大模型规模持续增长的关键驱动因素是什么？",
    "options": [
      "算法复杂度的降低和更小、高质量的数据集",
      "对模型伦理和公平性的严格要求",
      "计算能力的提升、训练数据的增多以及更复杂的模型架构",
      "仅依赖于对Transformer架构的持续优化"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档明确指出，模型规模的增长背后有三大核心原因：首先是硬件技术（如GPU、TPU）和计算平台的进步提供了更强的计算能力；其次是海量多模态数据的可用性为训练提供了丰富的数据来源；最后是除了Transformer之外，混合专家模型等更复杂的模型架构也在不断涌现。",
    "source": "文档中「1. 模型规模与计算能力的持续增长」部分",
    "hint": "文档中提到了模型规模提升的背后有几个方面的原因，可以从硬件、数据和模型结构三个角度思考。"
  },
  {
    "question": "文档中提到的“多模态学习”（Multi-modal learning）趋势，其核心特征是什么？",
    "options": [
      "模型专注于单一类型的数据（如纯文本）以达到极致的性能",
      "模型通过奖励机制不断优化其在一系列决策中的表现",
      "模型能在一个训练过程中同时学习并完成多个不同的任务",
      "模型能够在一个统一的框架下同时处理和理解多种不同类型的数据，如文本、图像和语音"
    ],
    "correctAnswerIndex": 3,
    "explanation": "多模态学习的核心是让AI大模型能够同时处理和整合来自不同“模态”（即不同类型）的数据。例如，文档中提到的CLIP模型就是将图像和文本结合起来进行理解。这与仅处理多种任务的“多任务学习”有所区别。",
    "source": "文档中「2. 从单一任务向多任务和多模态模型发展」部分",
    "hint": "请思考“模态”这个词的含义，它通常指代不同类型的信息来源，比如视觉、听觉或文字。"
  },
  {
    "question": "自监督学习（Self-supervised learning）相比于传统的监督学习，其最显著的优势是什么？",
    "options": [
      "它能够在不依赖大量人工标注标签的情况下，从未标注的数据中自动学习特征",
      "它的训练速度总是比监督学习快几个数量级",
      "它完全不需要任何数据即可进行训练",
      "它专门用于处理图像数据，不适用于文本"
    ],
    "correctAnswerIndex": 0,
    "explanation": "文档明确指出，与依赖大量昂贵的人工标注数据的监督学习不同，自监督学习的核心优势在于它能“通过从未标注的数据中自动提取有意义的特征进行学习”。这使得利用互联网上的海量数据进行预训练成为可能，如BERT和GPT模型。",
    "source": "文档中「3. 自监督学习的应用扩展」部分",
    "hint": "思考一下在获取海量数据时，什么工作是最昂贵和最耗时的？自监督学习正是为了解决这个问题。"
  },
  {
    "question": "“绿色AI”这一趋势主要关注解决AI大模型的哪个问题？",
    "options": [
      "提高模型在所有任务上的准确率",
      "确保模型生成的内容符合道德规范",
      "优化模型效率，以减少其巨大的能源消耗和碳足迹",
      "增加模型的参数数量以提升其通用能力"
    ],
    "correctAnswerIndex": 2,
    "explanation": "随着模型规模的急剧扩大，其计算和存储需求也随之增加，带来了巨大的能源消耗。文档指出，“绿色AI”的目标正是“优化AI模型的效率，减少其能源消耗和碳足迹”，例如通过模型压缩、高效硬件等技术手段来实现。",
    "source": "文档中「4. 模型优化与绿色AI」部分",
    "hint": "“绿色”这个词通常与环境保护和资源效率有关。思考一下大规模计算对环境可能产生的影响。"
  },
  {
    "question": "文档提到了强化学习（RL）与大模型的结合。这种结合主要增强了大模型在哪方面的能力？",
    "options": [
      "从海量未标注数据中学习通用知识",
      "同时处理图像和文本等多模态输入",
      "在复杂环境中进行策略学习和优化决策的能力",
      "生成更具创意和多样性的文本内容"
    ],
    "correctAnswerIndex": 2,
    "explanation": "强化学习通过“奖励机制”来训练模型不断优化其决策过程。当与知识渊博的大模型结合时，它能帮助模型学习更复杂的策略，从而在需要连续决策的复杂任务（如游戏、机器人、自动驾驶）中表现更佳，核心是提升决策能力。",
    "source": "文档中「5. 强化学习与大模型结合」部分",
    "hint": "强化学习的核心机制是通过“奖励”和“惩罚”来学习，这种机制最适合用于哪一类任务？"
  },
  {
    "question": "为什么“AI伦理与公平性”成为AI大模型发展中的一个重要议题？",
    "options": [
      "因为AI伦理问题会直接导致模型参数规模无法继续扩大",
      "因为大模型在训练过程中可能学习并放大训练数据中已有的社会偏见，导致不公平的决策",
      "因为不道德的AI模型会消耗更多的计算资源",
      "因为只有符合伦理的模型才能被开源"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档指出，大模型从海量数据中学习，而这些数据本身可能包含人类社会既有的偏见。模型在学习时不仅会复现这些偏见，甚至可能将其放大，导致不公平的结果，加剧社会不平等。因此，解决AI的偏见、确保其公平性和透明度至关重要。",
    "source": "文档中「6. AI伦理与公平性」部分",
    "hint": "当一个模型从反映人类社会的海量数据中学习时，它可能会学到哪些我们不希望它拥有的“坏习惯”？"
  },
  {
    "question": "文档中所述的“AI模型的民主化”趋势，具体体现在哪些方面？",
    "options": [
      "由政府机构主导所有AI大模型的开发和应用",
      "专注于为少数科技巨头提供更强大的定制化模型",
      "强制要求所有AI模型在做出决策前进行公开投票",
      "通过开源模型和低代码/无代码平台，降低AI技术的使用门槛"
    ],
    "correctAnswerIndex": 3,
    "explanation": "“AI的民主化”旨在让强大的AI技术不再是少数科技巨头的专利，而是让更多的开发者、研究人员甚至非技术人员也能使用。文档中明确提到了两个主要途径：一是开源模型和工具（如GPT、BERT），二是简化使用流程的低代码/无代码平台。",
    "source": "文档中「7. AI模型的民主化与开放」部分",
    "hint": "“民主化”在这里的含义是让技术普及化、平民化，让更多人能够接触和使用。"
  }
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                '<tr><td>错误</td><td>' + wrongCount + '</td></tr>' +
                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
                '</table>' +
                '</div>' +
                '</div>' +
                '<div class="report-actions">' +
                '<button id="review-btn">回顾测验</button>' +
                '<button class="primary" onclick="location.reload()">再试一次</button>' +
                '</div>' +
                '</div>';

            let reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }
            quizContentEl.insertAdjacentHTML('beforeend', finalScoreHtml);
            document.getElementById('review-btn').addEventListener('click', reviewQuiz);
        }

        function reviewQuiz() {
            const reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }

            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  